Abstract
A holistic classification system for off-line recognition of legal amounts in checks is described in this paper. The binary images obtained from the cursive words are processed following the human visual system, employing a Hough transform method to extract perceptual features. Images are finally coded into a bidimensional feature map representation. Multilayer perpeptrons are used to classify these feature maps into one of the 32 classes belonging to the CENPARMI database. To select a final classification system, ROC graphs are used to fix the best threshold values of the classifiers to obtain the best tradeoff between accuracy and misclassification.
Thanks to the Spanish CICYT under contract TIC2002-04103-C03-03 and the Generalitat Valenciana under contract 20040479 for partial funding.
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Madhvanath, S., Govindaraju, V.: The role of holistic paradigms in handwritten word recognition. IEEE Trans. on PAMI 23, 149–164 (2001)
Suen, C.Y., et al.: Computer recognition of unconstrained handwritten numerals. Special Issue of Proc. IEEE 7, 1162–1180 (1992)
Ruiz-Pinales, J., Lecolinet, E.: Cursive handwriting recognition using the Hough transform and a neural network. In: ICPR, pp. 231–234 (2002)
Bridle, J.S.: Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters. In: Advances in Neural Information Processing Systems, vol. 3. Morgan Kaufmann, San Francisco (1990)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley and Sons, New York (2001)
Ferri, C., Hernández-Orallo, J.: Cautious classifiers. In: ROCAI, pp. 27–36 (2004)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation, pp. 319–362. MIT Press, Cambridge (1986)
Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers. HP Technical Report HPL-2003-4, HP Labs (2003) (Revised March 2004)
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© 2005 Springer-Verlag Berlin Heidelberg
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Castro, M.J. et al. (2005). A Holistic Classification System for Check Amounts Based on Neural Networks with Rejection. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_45
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DOI: https://doi.org/10.1007/11590316_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
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